Evaluation of focusing performance in an assay analysis system

ABSTRACT

Methods, storage mediums and systems (MS&amp;S) are provided which successively image an imaging region of an assay analysis system (AAS) as particles are loaded into the imaging region, generate a frequency spectrum of each image via a discrete Fourier transform, integrate a same coordinate portion of each frequency spectrum and terminate the loading of particles upon computing an integral which meets preset criterion. In addition, MS&amp;S are provided which send a signal indicative of whether enough particles are in an imaging region for further processes by an AAS based on the magnitude of integral calculated from an image&#39;s frequency spectrum. MM&amp;S are also provided such that the steps of generating a frequency spectrum of each image and integrating a portion of each frequency spectrum are replaced by generating a convolved spatial image with a filter kernel and integrating a same coordinate portion of each convolved spatial image.

The present application is a continuation of U.S. patent applicationSer. No. 13/184,100, filed Jul. 15, 2011, which claims the benefit ofU.S. Provisional Application No. 61/364,879, filed Jul. 16, 2010. Eachof these applications is incorporated by reference herein in itsentirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention generally relates to methods, storage mediums, andsystems for analyzing particle quantity and distribution within animaging region of an assay analysis system and, further, to methods,storage mediums, and systems for evaluating the performance of afocusing routine performed on an assay analysis system.

2. Description of the Related Art

The following descriptions and examples are not admitted to be prior artby virtue of their inclusion within this section.

Fluid assays are used for a variety of purposes, including but notlimited to biological screenings and environmental assessments. Often,particles are used in fluid assays to aid in the detection andquantification of one or more analytes of interest within a sample. Inparticular, particles provide substrates for carrying reagentsconfigured to react with analytes of interest within a sample such thatthe analytes may be detected. In some cases, a multiplexing scheme isemployed in assay analysis systems such that multiple analytes may beevaluated in a single analysis process for a single sample. Tofacilitate a multiplexing scheme, particles are configured intodistinguishable groups and each group is used to indicate the presence,absence, and/or amount of a different analyte in an assay. The differentparticle subsets may be distinguishable, for example, by differentfluorescent dyes and/or different concentrations of dyes absorbed intoparticles and/or bound to the surface of particles. In addition oralternatively, the size of particles among the different subsets mayvary. In any case, optical imaging instruments may be used to analyzefluid assays induced with particles. More specifically, optical imaginginstruments may be configured to image particles within an illuminatedregion of a chamber in which an assay is introduced and may be furtherconfigured to analyze the imaged particles for the detection andquantification of one or more analytes of interest.

It is often advantageous to monitor the quantity of particles introducedinto the chamber to insure an appropriate amount is in the imagingregion for analysis of the sample. In particular, if an imaging regionis overpopulated with particles, at least some of the particles willcrowd each other causing them to reflect each other's light and falselyconvey a brighter intensity. On the contrary, if the number of particleswithin an imaging region is not enough to constitute statisticallysignificant data regarding the detection and quantification of analytesof interest within a sample (which may be particularly applicable inmultiplexing schemes), then the processes of imaging the particles andprocessing the data acquired therefrom are performed in vain. In somecases, the number of particles within the imaging region (particularlywhen there is not enough particles within the imaging region) may affectthe accuracy of an autofocus routine performed on the imaging system,which in turn will affect the resolution of any image taken andconsequently skew data acquired therefrom.

In many instances, the number of particles delivered into the imagingchamber is estimated based on expected particle densities present in thesample volume. Particle densities, however, can vary substantially fromsample to sample and, thus, such a technique requires the particledensity of a sample to be known and inputted into a system prior toinjecting the sample into the imaging chamber. Some assay analysistechniques involve counting particles within an imaging chamber. Forexample, spatial-domain image analysis is often performed usingthresholding for edge or peak detection of particles. Thresholding,however, can become complicated if particle brightness variessignificantly (such as in a multiplexing scheme). Furthermore, once anedge or peak has been detected, a neighborhood of pixels must beassembled that identifies an imaged particle, which can sometimes betime-consuming. Thus, spatial-domain image analysis is not generallyconsidered advantageous for monitoring the quantity of particlesintroduced into a chamber.

In an optical imaging instrument that must accurately measure the amountof fluorescent light emitted from each observed particle, particledistribution within an imaging region is as important as quantity. Inparticular, similar to the overpopulation of particles within an imagingregion, particles that are clustered together may induce measurablereflections and falsely convey a brighter intensity. Moreover, lightcollected from a cluster of particles is generally difficult todifferentiate on a particle-by-particle basis. To overcome this problem,particle clusters will often be ignored during analysis. Therefore, twoimaging volumes containing an equal population of particles butdifferent distributions will yield different amounts of useful data. Inorder to identify the particle clusters, spatial-domain image analysisas described above for particle counting is often performed. Such atechnique, however, is generally not used to determine particledistribution in an imaging volume nor would it be considered a viableoption, particularly as particles are being introduced into an imagingchamber, due to its time constraints.

In addition to the number of particles arranged within an imaging regionof an optical assay analysis system, the configuration of the systemaffects the accuracy of the data obtained from an image. In particular,it is important that the focal position of the photosensitive detectionsubsystem is optimized such that image resolution is optimized andaccurate data is obtained. In light of its importance, many opticalanalysis systems employ an automated routine for periodically optimizingthe focal position of its photosensitive detection subsystem. In manycases, however, characteristics and/or operation of an optical analysissystem may change over time and, in some embodiments, the changes mayaffect a routine's ability to optimize a focal position of aphotosensitive detection subsystem.

Accordingly, it would be beneficial to develop methods, programinstructions, and systems for evaluating the performance of a focusingroutine performed on an optical assay analysis system. Furthermore, itwould be desirable to develop methods, program instructions, and systemsfor analyzing particle quantity and distribution within an imagingregion of an optical assay analysis system, particularly as particlesare delivered into the imaging region. More specifically, it would beadvantageous to develop methods, program instructions, and systems foranalyzing the quantity of particles within an imaging chamber to insurean appropriate amount is present for further processes conducted by thesystem.

SUMMARY OF THE INVENTION

The following description of various embodiments of methods, storagemediums, and systems for analyzing particle quantity and distributionwithin an imaging region of an assay analysis system and variousembodiments of methods, storage mediums, and systems for evaluating theperformance of a focusing routine performed on an assay analysis systemis not to be construed in any way as limiting the subject matter of theappended claims.

Embodiments of the methods, storage mediums, and systems includeconfigurations for successively imaging an imaging region of an assayanalysis system as particles are loaded into the imaging region,generating a frequency spectrum of each image via a discrete Fouriertransform upon formation of each image, and integrating a samecoordinate portion of each frequency spectrum. Alternatively, someembodiments of the methods, storage mediums, and systems, includeconfigurations for successively imaging an imaging region of an assayanalysis system as particles are loaded into the imaging region,generating a convolved spatial image of each image by convolving eachimage with a kernel (e.g., a band pass or a high pass filter). In somecases, the methods, storage mediums, and systems include configurationsfor terminating the loading of particles into the imaging region uponcomputing an integral that crosses a predetermined threshold. In otherembodiments, the methods, storage mediums, and systems includeconfigurations for tracking integrals calculated for the successivelygenerated frequency spectrums and terminating the loading of particlesupon detecting a change in integral magnitude less than a preset limitbetween two frequency spectrums generated in succession. Similarly, insome embodiments, the methods, storage mediums, and systems includeconfigurations for tracking integrals calculated for the successivelygenerated convolved spatial images and terminating the loading ofparticles upon detecting a change in integral magnitude less than apreset limit between two convolved spatial images generated insuccession.

Other embodiments of the methods, storage mediums, and systems includeconfigurations for generating, via a discrete Fourier transform, afrequency spectrum of an image generated by an assay analysis system andintegrating a portion of the frequency spectrum. The methods, storagemediums, and systems further include configurations for sending a firstsignal indicating enough particles are in the imaging region for furtherprocesses by the assay analysis system upon calculating an integralgreater than a predetermined threshold. In addition, the methods,storage mediums, and systems include configurations for sending a secondsignal indicating not enough particles are in the imaging region forfurther processes by the assay analysis system upon calculating anintegral less than the predetermined threshold.

Similarly, other embodiments of the methods, storage mediums, andsystems include configurations for generating convolved spatial imagegenerated by an assay analysis system and integrating a portion of theconvolved spatial image. The methods, storage mediums, and systemsfurther include configurations for sending a first signal indicatingenough particles are in the imaging region for further processes by theassay analysis system upon calculating an integral greater than apredetermined threshold. In addition, the methods, storage mediums, andsystems include configurations for sending a second signal indicatingnot enough particles are in the imaging region for further processes bythe assay analysis system upon calculating an integral less than thepredetermined threshold.

Yet other embodiments of the methods, storage mediums, and systemsinclude configurations for performing an automated routine to optimize afocal position of a photosensitive detection subsystem of an assayanalysis system and imaging particles arranged within an imaging regionof the assay analysis system subsequent to performing the automatedroutine. The methods, storage mediums, and systems further includeconfigurations for generating a frequency spectrum of the image via adiscrete Fourier transform and comparing a width of a primary lobe ofthe frequency spectrum at a designated brightness value to a benchmarkto evaluate the performance of the automated routine. The methods,storage mediums, and systems additionally include configurations forgenerating a convolved spatial image of the image comparing portion ofthe generated convolved spatial image to a threshold to evaluate theperformance of the automated routine.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and advantages of the invention will become apparent uponreading the following detailed description and upon reference to theaccompanying drawings in which:

FIG. 1 is a schematic diagram of an optical assay analysis imagingsystem;

FIG. 2 is an example of a frequency spectrum of an image of particlesgenerated via a discrete Fourier transform;

FIG. 3 is a cross-sectional view of the frequency spectrum shown in FIG.2;

FIGS. 4A and 4B are flowcharts of a method for analyzing particlequantity and distribution within an imaging region of an optical assayanalysis system as particles are loaded into the imaging region todetermine when to terminate the loading of particles;

FIG. 5 is a flowchart of a method for analyzing particle quantity withinan imaging region of an optical assay analysis system to determine ifenough are present for further processes by the assay analysis system;and

FIG. 6 is a flowchart of a method for evaluating the performance of afocusing routine performed on an optical assay analysis system.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Itshould be understood, however, that the drawings and detaileddescription thereto are not intended to limit the invention to theparticular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope of the present invention as defined by the appendedclaims.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Turning to the drawings, exemplary methods, storage mediums, and systemsfor analyzing particle quantity and distribution within an imagingregion of an assay analysis system are provided. In addition, exemplarymethods, storage mediums, and systems for evaluating the performance ofa focusing routine performed on an assay analysis system are provided.In particular, FIGS. 4 and 5 depict flowcharts for analyzing particlequantity and distribution within an imaging region of an assay analysissystem. FIG. 6, on the other hand, depicts a flowchart for evaluatingthe performance of a focusing routine performed on an assay analysissystem. An exemplary optical assay analysis system is depicted in FIG. 1having a storage medium which includes program instructions configuredto perform the processes outlined in the flowcharts depicted in FIGS.4-6. FIGS. 2 and 3 illustrate graphical representations of an exemplaryfrequency spectrum generated via a discrete Fourier transform for animage of particles. As described in more detail below, some embodimentsof the methods described herein involve generating a frequency spectrumof an image via a discrete Fourier transform and analyzing the frequencyspectrum to obtain information about the state of the assay analysissystem used to create the image. Other embodiments of the methodsdescribed herein involve generating a convolved spatial image of animage by convolving the image with a filter kernel. It is noted that themethods, storage mediums, and systems described herein are notnecessarily limited to the illustrations of FIGS. 1-6. In particular,the systems described herein may include additional or alternativefeatures not shown in FIG. 1 and the frequency spectrums and convolvedspatial images generated using the systems described herein may be ofmany alternative forms, including but not limited to that depicted inFIGS. 2 and 3. In addition, the methods described herein may, in somecases, be configured to perform processes other than those depicted inFIGS. 4-6.

As noted above, FIG. 1 illustrates a schematic of an exemplary opticalassay analysis system, particularly the optic and control components ofthe system. In particular, FIG. 1 illustrates assay analysis system 10including an illumination subsystem comprising light sources 16, lenses17 and filters 18 configured to illuminate an imaging region of analysischamber 12. In addition, assay analysis system 10 includes aphotosensitive detection subsystem comprising imaging lens 20, filter/s22 and photosensitive detector 24 configured to image particles arrangedwithin the imaging region of analysis chamber 12 when illuminated.Furthermore, assay analysis system 10 may include processing unit 26operatively coupled (as denoted by the dotted lines in FIG. 1) todifferent components of the system, such as but not limited tocomponents of analysis chamber 12, the illumination subsystem and thephotosensitive subsystem. As described in more detail below, processingunit 26 includes a processor and a storage medium having programinstructions which are executable via the processor for performing anumber of operations on assay analysis system 10. It is noted that theoperative coupling of processing unit 26 is not limited to the depictionof dotted lines in FIG. 1. In particular, processing unit 26 may beoperatively coupled to any component of assay analysis system 10,including those not shown in FIG. 1 such as components of a fluidichandling system.

In general, analysis chamber 12 is configured to provide an opticallyclear path to its imaging region such that the region may be illuminatedand imaged by the respective illumination and photosensitive detectionsubsystems of assay analysis system 10 as discussed above. In addition,analysis chamber 12 may include any configuration suitable fortransporting a fluid assay having particles into and out of its imagingregion. As described below, the photosensitive detection subsystem ofassay analysis system 10 is configured to collect light reflected fromparticles in the imaging region and thus, the photosensitive detectionsubsystem is arranged on the same side of analysis chamber 12 as theillumination subsystem. In some cases, assay analysis system 10 mayinclude immobilization subsystem 14 disposed on the other side ofanalysis chamber 12 for immobilizing particles within the imaging regionof the chamber. In particular, particle immobilization may ensureparticles are in a stationary position and within the same plane forimaging. In other cases, however, immobilization subsystem 14 may beomitted from assay analysis system 10. In such embodiments, analysischamber 12 may be configured to distribute particles of a fluid assay ina single plane within its imaging region.

As shown in FIG. 1, the illumination subsystem of assay analysis system10 (i.e., light sources 16, lenses 17 and filters 18) may be configuredto illuminate the imaging region of analysis chamber 12 at an acuteangle relative to plane of the imaging region. Such an arrangementallows the photosensitive detection subsystem (i.e., imaging lens 20,filter/s 22 and photosensitive detector 24) to be centrally arrangedabove the imaging region of analysis chamber 12. Other configurations,however, are possible, particularly with the use of mirrors or otherdeflections devices as one skilled in the art would recognize. In anycase, light sources 16 may include any suitable light sources known inthe art, including but not limited to light emitting diodes (LEDs) andfilters 18 may be bandpass filters or any other suitable spectralfilters known in the art. In general, the light emitted by light sources16 may include light in any part of the visible and invisible wavelengthspectrums.

In some cases, light sources 16 (and additional light sources asrequired) may be configured to emit light having different wavelengthsor different wavelength bands (e.g., one of the light sources may beconfigured to emit red light and the other light source may beconfigured to emit green light). In this manner, the system may uselight sources 16, lenses 17 and filters 18 to sequentially illuminatethe particles with different wavelengths or different wavelength bandsof light. For example, red light may be used to excite classificationdyes that may be internal to the particles, and green light may be usedto excite reporter molecules coupled to the surface of the particles.Since the classification illumination is dark during reportermeasurements (i.e., in the above example, red light is not directed tothe particles while green light is directed to the particles), theanalyte measurement sensitivity of the system will not be reduced due tocrosstalk from out of band light.

Although assay analysis system 10 is shown in FIG. 1 having two lightsources, it is to be understood that the system may include any suitablenumber of light sources. In some embodiments, six light sources may bepositioned in a circumferential or hexagonal arrangement to direct lightonto the imaging plane of analysis chamber 12. In particular, lightsources 16 may be arranged such that each light source directs light toparticles in analysis chamber 12 from a different direction. In thismanner, the light sources may be configured to provide an illumination“ring”. Furthermore, although assay analysis system 10 is shown in FIG.1 having two lenses associated with each light source, it is to beunderstood that the system may include any suitable number of lenses foreach light source. For example, in some embodiments, the system mayinclude three refractive lenses for each light source to collect as muchlight from the light sources as possible and near-collimate it beforepresentation to a filter. Though a single normal refractive lens can beused, two or more lenses may be advantageous to increase the collectionangle and provide a more efficient illumination system.

As set forth above, the photosensitive detection subsystem of assayanalysis system 10 includes imaging lens 20, filter/s 22 andphotosensitive detector 24. In general, imaging lens 140 is configuredto image light scattered and/or fluoresced from particles disposedwithin the imaging region of analysis chamber 12 onto photosensitivedetector 24 via filter/s 22. Imaging lens 140 may include any suitablerefractive optical element known in the art and filter/s 22 may includeoptical bandpass filters or any other suitable spectral filter/s knownin the art. Photosensitive detector 24 may be a CCD, CMOS, or QuantumDot camera or any other suitable imaging device known in the art whichis configured to generate images. Although assay analysis system 10 isshown in FIG. 1 having a single photosensitive detector, the system mayinclude any suitable number of photosensitive detectors as well as anynumber of filters and lens to aid in the generation of images.

In some cases, assay analysis system 10 may include a device configuredto alternate different filters into the optical path of light exitingimaging lens 20, such as a filter wheel assembly, for example. In suchcases, each of the detection filters may be configured to transmit lightof a different wavelength or a different wavelength band. As such, thewavelength or wavelength band at which an image of particles is acquiredby photosensitive detector 24 may vary depending on the position of thefilter wheel assembly, which corresponds to the filter in the opticalpath of light exiting imaging lens 20. In this manner, a plurality ofimages of the particles may be formed sequentially by, imaging theparticles, altering the position of the filter wheel, and repeating theimaging and altering steps until images at each wavelength or wavebandof interest have been acquired by photosensitive detector 144. Thesystem shown in FIG. 1 may, therefore, be configured to generate aplurality or series of images representing the fluorescent emission ofparticles at several wavelengths of interest.

As set forth above and shown in FIG. 1, assay analysis system 10 may, insome embodiments, include processing unit 26 operatively coupled (asdenoted by the dotted lines in FIG. 1) to different components of thesystem, such as but not limited to components of analysis chamber 12,the illumination subsystem and the photosensitive subsystem. In othercases, however, assay analysis system 10 may not include a processingunit, but rather processing unit 26 may be a separate entity from assayanalysis system 10. In such embodiments, assay analysis system 10 may beconfigured for connection to a processing unit in any suitable mannerknown in the art (e.g., via transmission media or one or more electroniccomponents such as analog-to-digital converters). In any case,processing unit 26 may take various forms, including a personal computersystem, mainframe computer system, workstation, network appliance,Internet appliance, personal digital assistant (PDA), a digital signalprocessor (DSP), field programmable gate array (FPGA), or other similardevice.

In general, processing unit 26 includes a processor and a storage mediumhaving program instructions which are executable via the processor forperforming a number of operations on assay analysis system 10. Forexample, in addition to having program instructions to perform theprocesses set forth in FIGS. 4-6, processing unit 26 may be configuredto acquire (e.g., receive) image data from photosensitive detector 24and further process and analyze these images to determine one or morecharacteristics of particles such as a classification of the particlesand information about a reaction taken place on the surface of theparticles. The one or more characteristics may be output by theprocessing unit in any suitable format such as a data array with anentry for fluorescent magnitude for each particle for each wavelength.

In general, the term “storage medium,” as used herein, may refer to anyelectronic medium configured to hold one or more set of programinstructions, such as but not limited to a read-only memory, a randomaccess memory, a magnetic or optical disk, or magnetic tape. The term“program instructions” may generally refer to commands within a programwhich are configured to perform a particular function, such as analyzingparticle quantity and distribution within an imaging region of an assayanalysis system or evaluating the performance of a focusing routineperformed on an assay analysis system as described in more detail below.Program instructions may be implemented in any of various ways,including procedure-based techniques, component-based techniques, and/orobject-oriented techniques, among others. For example, the programinstructions may be implemented using ActiveX controls, C++ objects,JavaBeans, Microsoft Foundation Classes (“MFC”), or other technologiesor methodologies, as desired. Program instructions implementing theprocesses described herein may be transmitted over on a carrier mediumsuch as a wire, cable, or wireless transmission link.

As noted above, some embodiments of the methods described herein involvegenerating a frequency spectrum of an image via a discrete Fouriertransform and analyzing the frequency spectrum to obtain informationabout the state of the assay analysis system used to create the image.Alternatively stated, some embodiments of the invention usefrequency-domain techniques to characterize particle quantity anddistribution within an imaging volume as well as image focus.Theoretically, a particle may be represented by any number of pixels,specifically any number between one pixel, if the camera resolution isbelow the particle's size, or an infinite number of pixels, if thecamera resolution is infinite. Therefore, particle image topographylooks like a cube at the low end (i.e., a single pixel) and, at the highend (i.e., an infinite number pixels) due to the point spread functioneffect inherent of an imaging lens, the particle image topography willresemble the shape of the particle with its edges smoothed out into thebackground (e.g., the image topography of a spherical particle will looklike a Gaussian function at the high end). The concept of usingfrequency-domain techniques to characterize particle quantity anddistribution within an imaging volume as well as image focus isdescribed below in reference to a single pixel representing a particleand an infinite number of pixels representing a particle, but it is tobe understood that the concept may be applied to any number of pixelsrepresenting a particle. It is further noted that the actual transformused (e.g., a fast Fourier transform (FFT) or a discrete cosinetransform (DCT)) will be applied with integration limits beyond thephysical edges of the particle. As described throughout the disclosure,the embodiments of the invention are not limited these “frequencydomain” techniques. Rather, in some embodiments, the frequency contentof an image may be analyzed in the spatial domain (e.g., convolving theimage with a high-pass filter kernel to isolate the higher frequencycomponents in the spatial domain).

A frequency domain representation of an image contains a superpositionof the contributions of all particles in the image. For simplicity, animage frequency spectrum may be conceptualized with respect to a singleparticle such as set forth below. To mathematically conceptualize afrequency spectrum generated via a discrete Fourier transform, thetopography of a single rectangular pixel representing a particle, or anycuboid, may be represented by the following 2-dimensional step function:f(t,z)=∫_(−T/2) ^(T/2)∫_(−Z/2) ^(Z/2) AdtdzIn such a function, t and z are a coordinate system, T and Z arerespectively the horizontal and vertical lengths of the rectangularpixel and A is the brightness value of the pixel. The Fourier Transformof the above equation is:F(u,v)=∫_(−T/2) ^(T/2)∫_(−Z/2) ^(Z/2) Ae ^(−j2π(ut+uz)) dtdzresulting in a frequency spectrum that is defined by the following 2-Dsinc function:

${{F( {u,v} )}} = {{ATZ}{\frac{\sin( {\pi\;{uT}} )}{( {\pi\;{uT}} )}}{\frac{\sin( {\pi\;{vZ}} )}{( {\pi\;{vZ}} )}}}$

The peaks of such a sine function correspond to harmonics or multiplesof the fundamental spatial frequency and the peak to peak spacing in thefrequency domain is dependent on the edge lengths of the cuboid in thespatial domain before transformation. It is noted that the frequencyspectrum of |F(u,v)| will contract if the size of the pixel increases orif the number of pixels representing a particle increases. The frequencyspectrum of any shaped particle is related to the frequency spectrum ofa rectangle since any single radial slice through the particle resultsin a step function. For example, the projection of a spherical particleon a 2-D plane can be decomposed as an infinite number of radial slicesthat each define a rectangle and, thus, the resultant spectrum alsoconsists of equally spaced peaks, or harmonics. These same concepts maybe applied to a high resolution image of a particle (i.e., cases inwhich an infinite number of pixels represent a particle). For example, ahigh resolution image of a spherical particle will generally appear as acircle without distinct edges as it just gradually fades into thebackground (i.e., similar to a Gaussian function). Without distinctedges, the harmonic content will be minimal and the frequency spectrumof such an image will itself be very close to a Gaussian function. Inthe case of a perfect spatial Gaussian shape, the discrete Fouriertransform is Gaussian, with the widths of the two Gaussians (spatial andspectral) being inversely related.

Most particles being imaged, spheres included, will classify somewherebetween a square and a Gaussian. Because infinite resolution is notpossible, their spectrums will most likely exhibit harmonics in thedirection of the edges caused by the pixels themselves, in addition tothe shape of the particle. The methods described herein, thus, may beapplied to a number of different shaped molecules by monitoring thebrightness of the lobes, or harmonics, of their spectrums. It isparticularly noted that the methods described herein may be applied toparticles of any shape and, thus, are not limited to applications ofspherical particles. A graph of an exemplary frequency spectrum of aspherical particle is shown in FIG. 2 having a primary lobe 27 and aradial harmonic 28. A cross-sectional view or profile of the graphdepicted in FIG. 2 is shown in FIG. 3. FIGS. 2 and 3 are used inconjunction with the descriptions of the methods outlined in FIGS. 4-6,specifically in reference to processes of analyzing a frequency spectrumof an image. Although the frequency spectrum depicted in FIGS. 2 and 3includes a single radial harmonic 28, it is noted that the frequencyspectrums considered for the methods described herein are notnecessarily so limited. In particular, frequency spectrums having morethan one harmonic may be considered for the methods described herein. Itis noted that, just as with a spectrum of a square or a circle, primarylobe 27 will contract if the sphere's diameter increases.

In general, any type of particle may be used for the methods describedherein. In some cases, particles serving as vehicles for molecularreactions may be particularly applicable for the methods describedherein. Exemplary molecular reaction particles which are used in imagingsystems include xMAP® microspheres, which may be obtained commerciallyfrom Luminex Corporation of Austin, Tex. The term “particle” is usedherein to generally refer to microparticles, microspheres, polystyrenebeads, quantum dots, nanodots, nanoparticles, nanoshells, beads,microbeads, latex particles, latex beads, fluorescent beads, fluorescentparticles, colored particles, colored beads, tissue, cells,micro-organisms, organic matter, non-organic matter, or any otherdiscrete substrates or substances known in the art. Any of such termsmay be used interchangeably herein.

Turning to FIGS. 4A and B, method are provided for analyzing particlequantity and distribution within an imaging region of an optical assayanalysis system as particles are loaded into the imaging region todetermine when to terminate the loading of particles into the imagingregion. As shown in block 30 of FIG. 4A, an embodiment of a methodincludes successively imaging an imaging region of an assay analysissystem as particles are loaded into the imaging region. The frequency ofimaging may be preset and may generally be any frequency deemedappropriate for the assay analysis system, particularly depending on theflow rate the fluid assay is introduced into the imaging region, theprocessing speed of the photosensitive detection subsystem of the assayanalysis system, and the desired precision for identifying a point atwhich particles start to accumulate and/or cluster within the imagingregion. An exemplary frequency of imaging may be between every 1 to 5milliseconds. A frequency spectrum of each image is generated via adiscrete Fourier transform as noted in block 32. The frequency spectrumsare specifically generated upon formation of each image (i.e.,immediately after the formation of each image) such that the routineoutlined in FIG. 4A is performed in real-time (i.e., as particles arebeing loaded into the imaging region). The frequency spectrums may begenerated via a fast Fourier transform (FFT) or a discrete cosinetransform (DCT), both of which are categorized as discrete Fouriertransforms. In general, FFTs are considered a more complete frequencyanalysis tool than DCTs, but DCTs are much faster to compute than FFTs.Thus, there is generally a trade-off of which transform to employ.

The method continues to block 34 in which the same coordinate portion ofeach frequency spectrum is integrated. The “same coordinate portion”generally refers to the same, two-dimensional or three-dimensionalportion of each frequency spectrum as addressed by the spectrums'coordinates. The same coordinate portion may be any portion of thefrequency spectrums, but for reasons set forth below, it may beadvantageous for the same coordinate portion to be within a highfrequency portion of each spectrum. As further noted below, it may beadvantageous for the same coordinate portion to include a portion of aharmonic within each frequency spectrum. In general, each spectrum willget brighter as more particles populate the imaging volume. At somepoint during the transfer of particles into the imaging region, theparticles will begin to crowd around and/or cluster with other particlesand, as they do, the frequency spectrums will begin to be biased towardsthe lower frequencies since reflections off of many of the particleedges that contribute to the higher frequencies of the spectrum will besmoothed, reduced, or removed. In other words, the brightness gradientat the higher frequencies of the frequency spectrums will start todecrease and the brightness gradient at the lower frequencies of thefrequency spectrums will increase at some point since a higherproportion of the brightness contributed to the spectrum by incomingparticles will register in the lower frequencies.

Based on such changes to the frequency spectrums, a portion of thefrequency spectrums may be monitored (i.e., by taking an integral ofsuch a portion) to determine at which point to terminate the loading ofparticles within the imaging region, specifically to inhibit or minimizethe crowding and/or clustering of particles within the imaging region.In other words, a portion of the frequency spectrums may be monitored toindicate when the imaging volume is “full” but not overcrowded. Thistechnique is very powerful because it requires very little knowledgeabout the particles being loaded into the volume and it can identify theproper “load point” for an infinite number of distributions withoutadjustment. Since the accumulation and/or or clustering of particleswithin the imaging region will facilitate a change to the brightnessgradients in the higher frequency portion and the lower frequencyportion of the spectrums, portions within either the higher frequencyportion or the lower frequency portion may be monitored to terminate theloading of particles within the imaging region. In some cases, however,the lower frequency portions of the spectrums may contain increases inbrightness caused by rising levels of background light and, thus,decreases of the brightness gradient in the lower frequency portions ofa spectrum due to particle accumulation and/or clustering in the imagingregion may be slight. Since the high frequency portions of the spectrumare not indicative of levels of background light in an image, it may beadvantageous, in some cases, for the same coordinate portion integratedat block 34 to be within a high frequency portion of each spectrum.

In general, the primary lobe of a frequency spectrum represents the lowfrequency portion of light within an image and the portions of thespectrum extending out from the primary lobe, particularly theharmonics, represent the high frequency portion of light within animage. For example, in reference to FIGS. 2 and 3, primary lobe 27represents the low frequency portion of the image and harmonic 28represents the high frequency portion of the image. As such, in someembodiments, the same coordinate portion integrated at block 34 mayinclude a portion of a harmonic within each frequency spectrum. In anycase, in embodiments in which the high frequency portion of thespectrums are monitored for the integration process outlined in block34, the same coordinate portion may include any portion of the spectrumoutward from the primary lobe. For instance, the same coordinate portionmay, in some embodiments, be the entire area of the spectrum outwardfrom the primary lobe. In other cases, the same coordinate portion maybe a line extending from the base of the primary lobe out to the cornerof the spectrum. Other portions of the high frequency portion of thespectrum may also be considered for the integral computations.

In some cases, the same coordinate portion integrated at block 34 may bepreset before the method outlined in FIG. 4A commences. In otherembodiments, however, the method may include analyzing at least thefirst frequency spectrum generated in the routine to select a basispoint from which to establish the same specified portion of eachfrequency spectrum to be integrated, as shown in block 40. Such aprocess may generally include analyzing at least the first frequencyspectrum to determine the extent of the primary lobe and selecting thebasis point based on thereon, such as at the base of the primary lobe(i.e., the low point between the primary lobe and the harmonic) or thebasis point at a point offset and outward from the base of the primarylobe. An exemplary analysis routine may involve computing a movingaverage of brightness between a center of the spectrum and a cornerpoint of the spectrum to determine the base of the primary lobe,specifically analyzing the moving average computations and assigning thebase of the primary lobe when the moving average first starts toincrease. Other manners of determining the base of the primary lobe,however, may be used.

In any case, the process of analyzing a frequency spectrum to select abasis point for establishing the portion of each frequency spectrum tobe integrated may, in some embodiments, be done solely at the firstgenerated frequency spectrum. In other cases, the process of analyzing afrequency spectrum to select a basis point for establishing the portionof each frequency spectrum to be integrated may be done at eachfrequency spectrum generated or, alternatively, may be done in aperiodic manner relative to the generation of the frequency spectrums,such as but not limited to every other frequency spectrum which isgenerated. It is noted that the process of analyzing at least the firstgenerated frequency spectrum to select a basis point for establishingthe portion of each frequency spectrum to be integrated is optional and,thus, block 40 may be omitted from the method in some cases. Block 40 isoutlined by a dotted line in FIG. 4A to indicate that it is an optionalprocess.

Regardless of whether the same specified portion of the frequencyspectrums integrated for block 34 is preset or established by analysisof at least one of the frequency spectrums, the method outlined in FIG.4A includes terminating the loading of particle into the imaging regionupon computing an integral which meets a criterion preset for thesystem. The termination of particle loading may include any manner knownin the art, such as but not limited to closing a valve at any point inthe fluid transmission lines feeding the imaging region or halting apump feeding the fluid assay. In some embodiments, particle loading maybe terminated upon computing an integral which crosses a predeterminedthreshold as noted in block 36. In general, the predetermined thresholdmay be a particular integral value, which may vary among applicationsand systems and may generally be set based on the brightness of thelight to be reflected from the particles and the desired precision foridentifying a point at which particles start to accumulate and/orcluster within the imaging chamber. In other cases, the loading ofparticles into the imaging region may be terminated upon detecting achange in integral magnitude less than a preset limit between frequencyspectrums generated in succession as noted in block 44. In someembodiments, the process outlined in block 44 may include terminatingthe loading of particles upon detecting a change in the rate of increaseof the successively computed integrals. In any case, as noted in block42, the method outlined in FIG. 4A may generally include tracking theintegrals calculated for the successively generated frequency spectrumsprior to terminating particle loading based upon detecting a change inintegral magnitude.

In some cases, the preset limit set forth in block 44 corresponding tothe change in integral magnitude by which to terminate the loading ofparticles may be a particular value preassigned to the system or assay.In particular, the preset limit may be set prior to the processing ofthe assay. In such embodiments, the preset limit may include anythreshold deemed applicable for the assay and/or system, depending onthe brightness of the light to be reflected from the particles and thedesired precision for identifying a point at which particles start toaccumulate and/or cluster within the imaging chamber. In other cases,the preset limit set forth in block 44 by which to terminate the loadingof particles may be based on changes in integral magnitude between twoor more of the frequency spectrums as noted in block 46. In particular,such a process may involve measuring changes in integral magnitudebetween two frequency spectrums measured in succession and setting thepreset limit to be the measured change or less than the measured change.

The process outlined in block 46 may be performed on the first twogenerated frequency spectrums and, in some cases, for each frequencyspectrum generated thereafter. Alternatively, the process may beperformed in a periodic manner relative to the generation of thefrequency spectrums, but not on all of the frequency spectrums. It isnoted that the process of setting the limit by which to terminateparticle loading based on changes in integral magnitude is optional and,thus, block 46 may be omitted from the method in some cases. Block 46 isoutlined by a dotted line in FIG. 4A to indicate that it is an optionalprocess. An exemplary manner for conducting the processes denoted inblocks 42, 46, and 44 includes graphing the integral magnitudes for theprocess of block 42 and monitoring a slope of the graph for the processof block 46. The process of monitoring the slope of the graph maygenerally be used to set the preset limit by which to terminate particleloading and, thus, upon detecting a change in the slope, the loading ofthe particles may be terminated as set forth in block 44.

FIG. 4B denotes an alternative method for analyzing particle quantityand distribution within an imaging region of an optical assay analysissystem as particles are loaded into the imaging region to determine whento terminate the loading of particles into the imaging region. Asdepicted in FIG. 4B, embodiments of the method presented proceed in asimilar manner to FIG. 4B. Moreover, the method proceeds by analyzingthe particle quantity and distribution within an imaging region of anoptical assay analysis system by analyzing the frequency content ofsuccessive images as particles are loaded into the imaging reason.Moreover, based on the frequency content of these successive images, itmay be determined when to terminate the loading of the particles.However, instead of performing the steps of generating the a frequencyspectrum of each image and integrating a coordinate portion of eachfrequency spectrum, as indicated in blocks 32 and 34, the embodiments ofthe method denoted in FIG. 4B analyze the images differently. Ratherthan converting the images to the frequency domain, the analysis if thefrequency content of the images is performed in the spatial domain.

Instead of generating a frequency spectrum and analyzing the higherfrequency portions of that frequency spectrum as described with respectto specific embodiments of FIG. 4A, embodiments of the method in FIG. 4Bproceed by generating a convolved spatial image by convolving each imagewith a convolution kernel as denoted in block 33. As discussed in detailabove, when analyzing an image of the particles that may be used withsome embodiments of the methods, the higher frequency components of theimages may be found in the edges of the particles. In preferredembodiments, convolving each successive image with the convolutionkernel may act as a filter designed to isolate various higher frequencycomponents. In some embodiments, the convolution kernel may beconfigured to act as a high pass filter—filtering out the lowerfrequency components of the images and leaving the higher frequencycomponents. In some embodiments, the convolution kernel may beconfigured to act a band pass filter—filtering out both lower and higherfrequency components to focus on a specific frequency band.

To apply a filter to a frequency spectrum in the frequency domain, thespectrum may be multiplied by a filter function. As is well known in theart, multiplication by a function in the frequency domain ismathematically equivalent to convolution of the inverse fouriertransform in the spatial domain. As such, in specific embodiments, theconvolution kernel be created based on the inverse Fourier transform ofthe filter function that would be applied to the image spectrum tofilter frequencies in the frequency domain. For example, the frequencycomponents left in the image after convolving the image with the filterkernel may correspond to the same “portions of the frequency spectrums”discussed with regard to blocks 34 and 40 above (e.g., a portion of thefrequency spectrum moving outward from the primary lobe). Specifically,as denoted in block 34 of FIG. 4A, the method proceeds, in someembodiments, by the “same coordinate portion” of each frequencyspectrum, so that that portion may be integrated. In some embodiments,filter kernel may be created based on the inverse Fourier transform of afilter function designed to keep frequency components in thesecoordinate portions. Effectively then, convolving the filter kernel withthe image to produced the convolved spatial image will have the sameeffect as isolating certain frequencies in the frequency domain.

Thus, integrating at least a portion of the convolved spatial image asdenoted in block 35 will provide information regarding the frequencycontent of the images. As described in detail above, based on suchchanges to the frequency content of the successive images (as calculatedby the integral of the convolved spatial image), it may be determined atwhich point to terminate the loading of particles within the imagingregion, or specifically to inhibit or minimize the crowding and/orclustering of particles within the imaging region.

The remaining blocks of the embodiments of the method in FIG. 4B proceedsimilarly to the embodiments described with respect to FIG. 4A. That is,the embodiments of blocks 31, 33, 43, 45, and 47 correlate directly tothe descriptions of their respective blocks 30, 32, 42, 44, and 46described in FIG. 4A. The specifics of the various blocks are notrepeated for the sake of brevity.

FIG. 5 denotes a method for analyzing particle quantity and distributionwithin an imaging region of an optical assay analysis system todetermine if enough Particles are present in the imaging region forfurther processes by the assay analysis system. As shown in blocks 50and 52 of FIG. 5, the method includes generating a frequency spectrum ofan image via a discrete Fourier transform and integrating a portion ofthe frequency spectrum. As with the frequency spectrum generation stepdescribed in reference to FIG. 4A, the frequency spectrum for block 50may be generated via a fast Fourier transform (FFT) or a discrete cosinetransform (DCT). Further in reference to the description of FIG. 4A, theintegration step in block 52 may be applied to any portion of thefrequency spectrum, but it may be advantageous for the portion to bewithin a high frequency portion of the spectrum and to further include aportion of a harmonic within the frequency spectrum. The specifics withregard to the options for generating a frequency spectrum andintegrating a portion of the spectrum described in reference to blocks32 and 34 of FIG. 4A are referenced for blocks 50 and 52 of FIG. 5 andare not reiterated for the sake of brevity. Furthermore, as discussedwith respect to FIG. 4B, the steps of generating a frequency spectrum ofan image and integrating a portion of the frequency spectrum may beperformed instead by generating a convolved spatial image andintegrating at least a portion of the convolved spatial image.

As shown in FIG. 5, a determination is made at block 53 regardingwhether the integral computed at block 52 is greater than apredetermined threshold. The predetermined threshold may be anythreshold deemed applicable for the assay and/or system, depending onthe brightness of the light to be reflected from the particles and thedesired precision for determining whether a sufficient amount ofparticles are present in the imaging chamber for further processes ofthe assay analysis system. The further processes considered for themethod outlined in FIG. 5 may generally include any process which isparticle-dependent and, specifically, any process of which performancedepends on having a certain quantity of particles present in the imagingregion. Exemplary processes include but are not limited to analyzingparticles within the imaging chamber for the detection andquantification of one or more analytes of interest within a sample andanalyzing particles within the imaging chamber to perform an accurateautofocus routine. In any case, the method may include sending a signalindicating enough particles are in the imaging region for furtherprocesses by the assay analysis system upon calculating an integralgreater than the predetermined threshold as shown in block 54. On thecontrary, the method includes sending a different signal indicating notenough particles are in the imaging region for further processes by theassay analysis system upon calculating an integral less than thepredetermined threshold as denoted in block 56.

In some cases, the process may terminate upon either signal being sent.In other cases, however, the method may continue after the signalreferenced in block 56 is sent, specifically in an effort to increasethe particle quantity in the imaging chamber for further processes ofthe assay analysis system. In particular, as denoted by block 58, themethod may include loading additional particles into the imaging regionupon the signal referenced in block 56 is sent. In addition, the methodmay include generating an additional image of the imaging regionsubsequent to loading the additional particles into the imaging regionas denoted in block 59. As shown in FIG. 5, the method routes back torepeat the processes outlined in blocks 50, 52, 53, and 54 or 56. Insome cases, the processes outlined in blocks 58, 59, 50, 52 and 53 maybe reiterated until a signal is sent for block 54 or until apredetermined number of iterations is conducted. It is noted that theprocesses of loading additional particles into the imaging region andgenerating an additional image of the imaging region are optional and,thus, blocks 58 and 59 may be omitted from the method in some cases.Blocks 58 and 59 are outlined by dotted lines in FIG. 5 to indicate thatthey are optional processes.

FIG. 6 denotes a method for evaluating the performance of a focusingroutine performed on an assay analysis system. As shown in block 60 ofFIG. 6, the method includes performing an automated routine to optimizea focal position of a photosensitive detection subsystem of an assayanalysis system. Such a routine may generally be specific to the assayanalysis system and, may be even more specific to the photosensitivedetection subsystem included in the assay analysis system and, thus, mayvary widely. In any case, the method includes imaging particles arrangedwithin an imaging region of the assay analysis system subsequent toperforming the automated routine as denoted in block 62. Thereafter, afrequency spectrum of the image may be generated via a discrete Fouriertransform as shown in block 64. As with the frequency spectrumgeneration step described in reference to FIG. 4, the frequency spectrumfor block 64 may be generated via a fast Fourier transform (FFT) or adiscrete cosine transform (DCT).

In either case, the method continues to block 66 to compare a width of aprimary lobe of the frequency spectrum at a designated brightness valueto a benchmark to evaluate the performance of the automated routine. Insome cases, the benchmark may be derived experimentally apart from theassay analysis system. In other embodiments, the benchmark may be basedon a historic record of primary lobe widths at the designated brightnessvalue stored in the assay analysis system, specifically from frequencyspectrums generated from images previously obtained by the assayanalysis system. In particular, in some cases, primary lobe widths fromdifferent frequency spectrums may be stored within a database of theassay analysis system such that a historic record can be maintained. Therecording of the primary lobe widths may be performed each time themethod outlined in FIG. 6 is performed for an assay analysis system ormay be performed in a periodic manner relative to the number of timesthe method is conducted. Further to such a concept, the method outlinedin FIG. 6 (i.e., the processes referenced in blocks 60, 62, 64 and 66)may be repeated at a set frequency relative to the number of times theautomated routine is performed for the assay analysis system. The setfrequency may be any frequency deemed appropriate for the assay analysissystem, particularly depending on the desired precision for evaluatingthe performance of a focusing routine.

It will be appreciated to those skilled in the art having the benefit ofthis disclosure that this invention is believed to provide methods,storage mediums, and systems for analyzing particle quantity anddistribution within an imaging region of an assay analysis system. Inaddition, methods, storage mediums, and systems for evaluating theperformance of a focusing routine performed on an assay analysis systemare provided. Further modifications and alternative embodiments ofvarious aspects of the invention will be apparent to those skilled inthe art in view of this description. Accordingly, this description is tobe construed as illustrative only and is for the purpose of teachingthose skilled in the art the general manner of carrying out theinvention. It is to be understood that the forms of the invention shownand described herein are to be taken as the presently preferredembodiments. Elements and materials may be substituted for thoseillustrated and described herein, parts and processes may be reversed,and certain features of the invention may be utilized independently, allas would be apparent to one skilled in the art after having the benefitof this description of the invention. Changes may be made in theelements described herein without departing from the spirit and scope ofthe invention as described in the following claims.

What is claimed is:
 1. A method, comprising: performing an automatedroutine to adjust a focal position of a photosensitive detectionsubsystem of an assay analysis system; subsequent to performing theautomated routine, imaging particles arranged within an imaging chamberof the assay analysis system to create an image; generating a frequencyspectrum of the image via a discrete Fourier transform; and comparing awidth of a primary lobe of the frequency spectrum at a selectedbrightness value to a benchmark to evaluate a performance of theautomated routine to adjust the focal position of the photosensitivedetection subsystem.
 2. The method of claim 1, wherein the benchmark isspecific to the assay analysis system.
 3. The method of claim 1, whereinthe benchmark is based on a historic record of primary lobe widths atthe selected brightness value of frequency spectra generated from imagespreviously obtained by the assay analysis system.
 4. The method of claim1, further comprising repeating, at a selected frequency based on anumber of times the automated routine is performed for the assayanalysis system, the steps of imaging, generating a frequency spectrum,and comparing a width of a primary lobe of the generated frequencyspectrum to the benchmark.
 5. The method of claim 1, wherein thefrequency spectrum is generated via a fast Fourier transform.
 6. Themethod of claim 1, wherein the frequency spectrum is generated via adiscrete cosine transform.
 7. The method of claim 1, wherein the imageis based on a fluorescent material associated with the particles.
 8. Anassay analysis system, comprising: an imaging chamber configured toaccept a plurality of particles to be imaged; and a photosensitivedetection system configured to receive light emitted by the plurality ofparticles; wherein the assay analysis system is configured to: performan adjustment of a focal position of the photosensitive detectionsystem; create an image of the plurality of particles in the imagingchamber; generate a frequency spectrum of the image via a discreteFourier transform; and performing a comparison of a width of a primarylobe of the frequency spectrum to a benchmark to evaluate the adjustmentof the focal position of the photosensitive detection system.
 9. Theassay analysis system of claim 8, wherein the frequency spectrum of theimage is a two-dimensional frequency spectrum.
 10. The assay analysissystem of claim 8, wherein the discrete Fourier transform is selectedfrom the group consisting of fast Fourier transform and discrete cosinetransform.
 11. The assay analysis system of claim 8, further comprisingan illumination subsystem.
 12. The assay analysis system of claim 11,wherein the image is based on fluorescence emitted by the plurality ofparticles in response to illumination by the illumination subsystem. 13.The assay analysis system of claim 8, wherein the adjustment is based onan automated adjustment routine.
 14. The assay analysis system of claim8, wherein the comparison is based on a selected brightness value. 15.An assay analysis system, comprising: an imaging chamber configured toaccept a plurality of particles to be imaged; and a photosensitivedetection system configured to receive light emitted by the plurality ofparticles; wherein the assay analysis system is configured to: create animage of the plurality of particles in the imaging chamber; generate afrequency spectrum of the image; and evaluate a focal position of thephotosensitive detection system by comparing a width of a primary lobeof the frequency spectrum to a benchmark.
 16. The assay analysis systemof claim 15, wherein the assay analysis system is further configured toperform an automated focusing routine to adjust the focal position ofthe photosensitive detection system.
 17. The assay analysis system ofclaim 15, further comprising a storage medium having a database storedthereon, wherein the database includes a historic record of primary lobewidths, and wherein the benchmark is based on the database.
 18. Theassay analysis system of claim 15, wherein the frequency spectrum isbased on a fast Fourier transform of the image.
 19. The assay analysissystem of claim 15, wherein the frequency spectrum is based on adiscrete cosine transform of the image.